Empirically, we document norm growth in the training of transformer language models, including T5 during its pretraining. As the parameters grow in magnitude, we prove that the network approximates a discretized network with saturated activation functions.
Oct 19, 2020
Nov 7, 2021 · This suggests that the norm growth implicit in training guides transformers to approximate saturated networks, justifying studying the latter ( ...
The tendency of transformer parameters to grow in magnitude during training is studied to find that in certain contexts, GD increases the parameter $L_2$ ...
Sep 7, 2024 · This means increasing training accuracy over time enables the norm to increase. Empirically, we show that the norm grows continuously over ...
The tendency for transformer parameters to grow in magnitude during training is studied, and its implications for the emergent representations within self ...
Nov 17, 2021 · This paper found that the \ell_2 of transformer parameters grows during training, leading to a discretized network with saturated activation ...
Mar 7, 2023 · Effects of Parameter Norm Growth During Transformer Training: ... the parameter norm of transformers tends to grow over the course of training.
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Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent William Merrill∗† Vivek Ramanujan∗‡ Yoav Goldberg ...
According to Merrill et al. (2021) , neural networks learn successfully due to inductive biases introduced during training. Norm growth induces saturation in ...
Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent William Merrill, Vivek Ramanujan, Yoav Goldberg, Roy ...